The technique of forgetting in knowledge representation has been shown to be a powerful and useful knowledge engineering tool with widespread application. Yet, very little research has been done on how different policies of forgetting, or use of different forgetting operators, affects the inferential strength of the original theory. The goal of this paper is to define loss functions for measuring changes in inferential strength based on intuitions from model counting and probability theory. Properties of such loss measures are studied and a pragmatic knowledge engineering tool is proposed for computing loss measures using ProbLog. The paper includes a working methodology for studying and determining the strength of different forgetting policies, in addition to concrete examples showing how to apply the theoretical results using ProbLog. Although the focus is on forgetting, the results are much more general and should have wider application to other areas.

翻译：知识表示中的遗忘技术已被证明是一种强大且实用的知识工程工具，具有广泛的应用前景。然而，关于不同遗忘策略或不同遗忘算子的使用如何影响原始理论的推理强度，目前的研究还非常有限。本文的目标是基于模型计数和概率论的直观思想，定义用于衡量推理强度变化的损失函数。我们研究了此类损失度量的性质，并提出了一种实用的知识工程工具，用于使用ProbLog计算损失度量。本文不仅提供了研究和确定不同遗忘策略强度的工作方法，还通过具体示例展示了如何使用ProbLog应用理论结果。尽管研究重点是遗忘技术，但所得结果具有更广泛的普适性，应能应用于其他领域。